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Development of a Clinical-Based Prediction Model for Pediatric Intussusception: A Prospective Observational Study
Dongbum Suh1, Jin Hee Lee2, Seongyeon Oh3
1Department of Emergency Medicine, Seoul National University Bundang Hospital, Seongnam, Republic of Korea; Department of Emergency Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea.
Insights
A new clinical prediction model identifies children with intussusception using symptoms like fever absence and RUQ tenderness. This tool aids in timely ultrasound decisions for suspected intussusception in pediatric patients.
Area of Science:
- Pediatric Emergency Medicine
- Clinical Prediction Modeling
- Gastrointestinal Emergencies
Background:
- Intussusception is a critical pediatric condition causing bowel obstruction and ischemia.
- Accurate and timely diagnosis of intussusception is vital for effective treatment.
- Ultrasound is the primary diagnostic tool for intussusception in children.
Purpose of the Study:
- To develop a clinical prediction model for intussusception.
- The model utilizes history, symptoms, and physical examination findings.
- The goal is to identify pediatric patients requiring ultrasound evaluation for intussusception.
Main Methods:
- Prospective observational study conducted in a pediatric emergency department.
- Included children under 6 years old with suspected intussusception.
- Logistic regression was used to identify predictors and develop the model, with diagnostic performance assessed using sensitivity, specificity, PPV, NPV, and AUROC.
Main Results:
- Out of 83 patients, 34.9% were diagnosed with intussusception.
- Key predictors included absence of fever (aOR 4.4), intermittent abdominal pain/irritability (aOR 3.4), absence of diarrhea (aOR 8.0), and RUQ tenderness (aOR 8.2).
- The model achieved 66% sensitivity, 83% specificity, 68% PPV, 81.5% NPV, and an AUROC of 0.78, with 100% sensitivity and NPV at a score of ≥1.
Conclusions:
- A prediction model based on specific symptoms effectively identifies children at risk for intussusception.
- This model supports timely ultrasound decisions in pediatric patients with ambiguous abdominal symptoms.
- Improved diagnostic accuracy can lead to better patient outcomes for intussusception.
Background:
Intussusception, where 1 intestinal segment invaginates into another, causes bowel obstruction and ischemia in children, diagnosed via ultrasound. Early identification of patients requiring ultrasound for intussusception diagnosis is essential.
Objectives:
This study aims to develop a clinical prediction model for intussusception based on solely history, symptoms, and physical examination to identify patients needing ultrasound evaluation.
Methods:
This prospective observational study (September 2019-February 2024) in a pediatric emergency department, included children under 6 with suspected intussusception. Logistic regression identified predictors, and a clinical prediction model was developed. Diagnostic performance was evaluated using sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and the area under the receiver operating characteristic curve (AUROC).
Results:
Out of 83 patients, 29 (34.9%) were diagnosed with intussusception. Key predictors were absence of fever (adjusted odds ratio [aOR] 4.4), intermittent abdominal pain or irritability (aOR 3.4), absence of diarrhea (aOR 8.0), and right upper quadrant (RUQ) tenderness (aOR 8.2). The model, which includes absence of fever (1 point), intermittent abdominal pain or irritability (1 point), absence of diarrhea (2 points), and RUQ tenderness (2 points), demonstrated 66% sensitivity, 83% specificity, 68% PPV, 81.5% NPV, and an AUROC of 0.78, achieving 100% sensitivity and NPV at a threshold score of ≥1 point.
Conclusions:
The prediction model, based on absence of fever, intermittent abdominal pain/irritability, absence of diarrhea, and RUQ tenderness, effectively identifies children at risk for intussusception. It supports timely ultrasound decisions in children with ambiguous abdominal symptoms, improving patient outcomes.
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